Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations916
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory365.4 KiB
Average record size in memory408.5 B

Variable types

Numeric6
Categorical2
Text3

Alerts

4s is highly overall correlated with Balls and 1 other fieldsHigh correlation
6s is highly overall correlated with RunsHigh correlation
Balls is highly overall correlated with 4s and 1 other fieldsHigh correlation
Match_Between is highly overall correlated with Match_no and 1 other fieldsHigh correlation
Match_no is highly overall correlated with Match_BetweenHigh correlation
Runs is highly overall correlated with 4s and 2 other fieldsHigh correlation
Team_Innings is highly overall correlated with Match_BetweenHigh correlation
Runs has 83 (9.1%) zerosZeros
4s has 287 (31.3%) zerosZeros
6s has 616 (67.2%) zerosZeros

Reproduction

Analysis started2024-11-04 11:05:17.049834
Analysis finished2024-11-04 11:05:27.084897
Duration10.04 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Match_no
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.669214
Minimum1
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-11-04T16:05:27.417906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q112
median25
Q337
95-th percentile47
Maximum48
Range47
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.042355
Coefficient of variation (CV)0.56922587
Kurtosis-1.2267707
Mean24.669214
Median Absolute Deviation (MAD)12
Skewness0.034900774
Sum22597
Variance197.18773
MonotonicityNot monotonic
2024-11-04T16:05:27.770612image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
47 40
 
4.4%
10 40
 
4.4%
2 22
 
2.4%
7 22
 
2.4%
13 22
 
2.4%
26 22
 
2.4%
28 22
 
2.4%
18 22
 
2.4%
27 22
 
2.4%
36 22
 
2.4%
Other values (38) 660
72.1%
ValueCountFrequency (%)
1 14
 
1.5%
2 22
2.4%
3 17
1.9%
4 18
2.0%
5 17
1.9%
6 20
2.2%
7 22
2.4%
8 16
 
1.7%
9 14
 
1.5%
10 40
4.4%
ValueCountFrequency (%)
48 17
1.9%
47 40
4.4%
46 17
1.9%
45 17
1.9%
44 22
2.4%
43 14
 
1.5%
42 18
2.0%
41 18
2.0%
40 22
2.4%
39 16
 
1.7%

Match_Between
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size71.4 KiB
South Africa vs Australia
80 
England vs Bangladesh
 
22
Pakistan vs Netherlands
 
22
Afghanistan vs England
 
22
Pakistan vs South Africa
 
22
Other values (42)
748 

Length

Max length27
Median length24
Mean length22.629913
Min length16

Characters and Unicode

Total characters20729
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngland vs New Zealand
2nd rowEngland vs New Zealand
3rd rowEngland vs New Zealand
4th rowEngland vs New Zealand
5th rowEngland vs New Zealand

Common Values

ValueCountFrequency (%)
South Africa vs Australia 80
 
8.7%
England vs Bangladesh 22
 
2.4%
Pakistan vs Netherlands 22
 
2.4%
Afghanistan vs England 22
 
2.4%
Pakistan vs South Africa 22
 
2.4%
Netherlands vs Bangladesh 22
 
2.4%
Australia vs Pakistan 22
 
2.4%
Australia vs New Zealand 22
 
2.4%
Australia vs England 22
 
2.4%
England vs Pakistan 22
 
2.4%
Other values (37) 638
69.7%

Length

2024-11-04T16:05:28.089194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vs 916
27.7%
australia 249
 
7.5%
africa 232
 
7.0%
south 232
 
7.0%
india 191
 
5.8%
england 180
 
5.4%
netherlands 179
 
5.4%
zealand 173
 
5.2%
new 173
 
5.2%
bangladesh 160
 
4.8%
Other values (4) 627
18.9%

Most occurring characters

ValueCountFrequency (%)
a 2882
13.9%
2396
 
11.6%
s 1813
 
8.7%
n 1681
 
8.1%
i 1140
 
5.5%
t 969
 
4.7%
l 941
 
4.5%
v 916
 
4.4%
d 883
 
4.3%
e 864
 
4.2%
Other values (18) 6244
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20729
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2882
13.9%
2396
 
11.6%
s 1813
 
8.7%
n 1681
 
8.1%
i 1140
 
5.5%
t 969
 
4.7%
l 941
 
4.5%
v 916
 
4.4%
d 883
 
4.3%
e 864
 
4.2%
Other values (18) 6244
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20729
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2882
13.9%
2396
 
11.6%
s 1813
 
8.7%
n 1681
 
8.1%
i 1140
 
5.5%
t 969
 
4.7%
l 941
 
4.5%
v 916
 
4.4%
d 883
 
4.3%
e 864
 
4.2%
Other values (18) 6244
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20729
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2882
13.9%
2396
 
11.6%
s 1813
 
8.7%
n 1681
 
8.1%
i 1140
 
5.5%
t 969
 
4.7%
l 941
 
4.5%
v 916
 
4.4%
d 883
 
4.3%
e 864
 
4.2%
Other values (18) 6244
30.1%

Team_Innings
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size59.5 KiB
Australia
120 
South Africa
109 
England
99 
Netherlands
98 
Bangladesh
90 
Other values (5)
400 

Length

Max length12
Median length11
Mean length9.3799127
Min length5

Characters and Unicode

Total characters8592
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEngland
2nd rowEngland
3rd rowEngland
4th rowEngland
5th rowEngland

Common Values

ValueCountFrequency (%)
Australia 120
13.1%
South Africa 109
11.9%
England 99
10.8%
Netherlands 98
10.7%
Bangladesh 90
9.8%
Sri Lanka 87
9.5%
New Zealand 84
9.2%
Pakistan 77
8.4%
India 77
8.4%
Afghanistan 75
8.2%

Length

2024-11-04T16:05:28.404532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-04T16:05:28.697218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
australia 120
10.0%
south 109
9.1%
africa 109
9.1%
england 99
8.3%
netherlands 98
8.2%
bangladesh 90
 
7.5%
sri 87
 
7.3%
lanka 87
 
7.3%
new 84
 
7.0%
zealand 84
 
7.0%
Other values (3) 229
19.1%

Most occurring characters

ValueCountFrequency (%)
a 1449
16.9%
n 861
 
10.0%
i 545
 
6.3%
l 491
 
5.7%
t 479
 
5.6%
s 460
 
5.4%
e 454
 
5.3%
d 448
 
5.2%
r 414
 
4.8%
h 372
 
4.3%
Other values (17) 2619
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1449
16.9%
n 861
 
10.0%
i 545
 
6.3%
l 491
 
5.7%
t 479
 
5.6%
s 460
 
5.4%
e 454
 
5.3%
d 448
 
5.2%
r 414
 
4.8%
h 372
 
4.3%
Other values (17) 2619
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1449
16.9%
n 861
 
10.0%
i 545
 
6.3%
l 491
 
5.7%
t 479
 
5.6%
s 460
 
5.4%
e 454
 
5.3%
d 448
 
5.2%
r 414
 
4.8%
h 372
 
4.3%
Other values (17) 2619
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1449
16.9%
n 861
 
10.0%
i 545
 
6.3%
l 491
 
5.7%
t 479
 
5.6%
s 460
 
5.4%
e 454
 
5.3%
d 448
 
5.2%
r 414
 
4.8%
h 372
 
4.3%
Other values (17) 2619
30.5%
Distinct146
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size63.3 KiB
2024-11-04T16:05:29.631439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length13.629913
Min length8

Characters and Unicode

Total characters12485
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)1.2%

Sample

1st rowJonny Bairstow
2nd rowDawid Malan
3rd rowJoe Root
4th rowHarry Brook
5th rowMoeen Ali
ValueCountFrequency (%)
mitchell 38
 
2.0%
van 36
 
1.9%
hasan 33
 
1.7%
david 31
 
1.6%
de 28
 
1.4%
mohammad 20
 
1.0%
glenn 20
 
1.0%
der 20
 
1.0%
josh 16
 
0.8%
kusal 16
 
0.8%
Other values (258) 1679
86.7%
2024-11-04T16:05:30.546185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1729
 
13.8%
1021
 
8.2%
e 794
 
6.4%
i 732
 
5.9%
n 730
 
5.8%
h 684
 
5.5%
r 616
 
4.9%
l 553
 
4.4%
s 509
 
4.1%
m 426
 
3.4%
Other values (44) 4691
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12485
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1729
 
13.8%
1021
 
8.2%
e 794
 
6.4%
i 732
 
5.9%
n 730
 
5.8%
h 684
 
5.5%
r 616
 
4.9%
l 553
 
4.4%
s 509
 
4.1%
m 426
 
3.4%
Other values (44) 4691
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12485
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1729
 
13.8%
1021
 
8.2%
e 794
 
6.4%
i 732
 
5.9%
n 730
 
5.8%
h 684
 
5.5%
r 616
 
4.9%
l 553
 
4.4%
s 509
 
4.1%
m 426
 
3.4%
Other values (44) 4691
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12485
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1729
 
13.8%
1021
 
8.2%
e 794
 
6.4%
i 732
 
5.9%
n 730
 
5.8%
h 684
 
5.5%
r 616
 
4.9%
l 553
 
4.4%
s 509
 
4.1%
m 426
 
3.4%
Other values (44) 4691
37.6%

Batting_Position
Real number (ℝ)

Distinct11
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3984716
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-11-04T16:05:30.786907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile11
Maximum11
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0359518
Coefficient of variation (CV)0.56237246
Kurtosis-1.0799941
Mean5.3984716
Median Absolute Deviation (MAD)2
Skewness0.23224721
Sum4945
Variance9.2170031
MonotonicityNot monotonic
2024-11-04T16:05:31.001531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 100
10.9%
2 100
10.9%
3 100
10.9%
4 98
10.7%
5 94
10.3%
6 88
9.6%
7 81
8.8%
8 73
8.0%
9 69
7.5%
10 60
6.6%
ValueCountFrequency (%)
1 100
10.9%
2 100
10.9%
3 100
10.9%
4 98
10.7%
5 94
10.3%
6 88
9.6%
7 81
8.8%
8 73
8.0%
9 69
7.5%
10 60
6.6%
ValueCountFrequency (%)
11 53
5.8%
10 60
6.6%
9 69
7.5%
8 73
8.0%
7 81
8.8%
6 88
9.6%
5 94
10.3%
4 98
10.7%
3 100
10.9%
2 100
10.9%
Distinct469
Distinct (%)51.3%
Missing2
Missing (%)0.2%
Memory size72.1 KiB
2024-11-04T16:05:31.446363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length49
Median length40
Mean length23.577681
Min length7

Characters and Unicode

Total characters21550
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique301 ?
Unique (%)32.9%

Sample

1st rowc Daryl Mitchell b Mitchell Santner
2nd rowc Tom Latham b Matt Henry
3rd rowb Glenn Phillips
4th rowc Devon Conway b Rachin Ravindra
5th rowb Glenn Phillips
ValueCountFrequency (%)
b 723
 
16.6%
c 484
 
11.1%
out 190
 
4.4%
not 146
 
3.4%
lbw 79
 
1.8%
mitchell 61
 
1.4%
hasan 48
 
1.1%
de 48
 
1.1%
run 44
 
1.0%
van 43
 
1.0%
Other values (283) 2484
57.1%
2024-11-04T16:05:32.300251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3436
15.9%
a 2236
 
10.4%
e 1191
 
5.5%
n 1105
 
5.1%
i 977
 
4.5%
b 961
 
4.5%
o 944
 
4.4%
t 883
 
4.1%
h 854
 
4.0%
r 794
 
3.7%
Other values (47) 8169
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3436
15.9%
a 2236
 
10.4%
e 1191
 
5.5%
n 1105
 
5.1%
i 977
 
4.5%
b 961
 
4.5%
o 944
 
4.4%
t 883
 
4.1%
h 854
 
4.0%
r 794
 
3.7%
Other values (47) 8169
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3436
15.9%
a 2236
 
10.4%
e 1191
 
5.5%
n 1105
 
5.1%
i 977
 
4.5%
b 961
 
4.5%
o 944
 
4.4%
t 883
 
4.1%
h 854
 
4.0%
r 794
 
3.7%
Other values (47) 8169
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3436
15.9%
a 2236
 
10.4%
e 1191
 
5.5%
n 1105
 
5.1%
i 977
 
4.5%
b 961
 
4.5%
o 944
 
4.4%
t 883
 
4.1%
h 854
 
4.0%
r 794
 
3.7%
Other values (47) 8169
37.9%

Runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct124
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.510917
Minimum0
Maximum201
Zeros83
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-11-04T16:05:32.659245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median15
Q338
95-th percentile92.75
Maximum201
Range201
Interquartile range (IQR)33

Descriptive statistics

Standard deviation30.788666
Coefficient of variation (CV)1.161358
Kurtosis4.0220039
Mean26.510917
Median Absolute Deviation (MAD)13
Skewness1.8807896
Sum24284
Variance947.94196
MonotonicityNot monotonic
2024-11-04T16:05:32.981210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83
 
9.1%
1 46
 
5.0%
4 37
 
4.0%
2 36
 
3.9%
10 29
 
3.2%
9 28
 
3.1%
5 26
 
2.8%
12 23
 
2.5%
11 23
 
2.5%
6 22
 
2.4%
Other values (114) 563
61.5%
ValueCountFrequency (%)
0 83
9.1%
1 46
5.0%
2 36
3.9%
3 20
 
2.2%
4 37
4.0%
5 26
 
2.8%
6 22
 
2.4%
7 21
 
2.3%
8 19
 
2.1%
9 28
 
3.1%
ValueCountFrequency (%)
201 1
0.1%
177 1
0.1%
174 1
0.1%
163 1
0.1%
152 1
0.1%
140 1
0.1%
137 1
0.1%
134 1
0.1%
133 1
0.1%
131 2
0.2%

Balls
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.021834
Minimum0
Maximum143
Zeros6
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-11-04T16:05:33.299132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median19
Q340.25
95-th percentile90.25
Maximum143
Range143
Interquartile range (IQR)32.25

Descriptive statistics

Standard deviation28.493381
Coefficient of variation (CV)0.98179118
Kurtosis1.6740675
Mean29.021834
Median Absolute Deviation (MAD)13
Skewness1.4487886
Sum26584
Variance811.87275
MonotonicityNot monotonic
2024-11-04T16:05:33.607220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 47
 
5.1%
3 31
 
3.4%
4 31
 
3.4%
6 28
 
3.1%
2 27
 
2.9%
7 26
 
2.8%
8 26
 
2.8%
5 25
 
2.7%
17 24
 
2.6%
13 23
 
2.5%
Other values (108) 628
68.6%
ValueCountFrequency (%)
0 6
 
0.7%
1 47
5.1%
2 27
2.9%
3 31
3.4%
4 31
3.4%
5 25
2.7%
6 28
3.1%
7 26
2.8%
8 26
2.8%
9 20
2.2%
ValueCountFrequency (%)
143 1
 
0.1%
140 1
 
0.1%
132 1
 
0.1%
128 1
 
0.1%
127 1
 
0.1%
124 1
 
0.1%
121 3
0.3%
120 1
 
0.1%
119 1
 
0.1%
118 1
 
0.1%

4s
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5338428
Minimum0
Maximum21
Zeros287
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-11-04T16:05:33.874588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile9
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1191198
Coefficient of variation (CV)1.2309839
Kurtosis4.3896219
Mean2.5338428
Median Absolute Deviation (MAD)1
Skewness1.8823944
Sum2321
Variance9.7289081
MonotonicityNot monotonic
2024-11-04T16:05:34.098808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 287
31.3%
1 177
19.3%
2 125
13.6%
3 89
 
9.7%
4 66
 
7.2%
6 37
 
4.0%
5 31
 
3.4%
7 25
 
2.7%
8 24
 
2.6%
9 18
 
2.0%
Other values (10) 37
 
4.0%
ValueCountFrequency (%)
0 287
31.3%
1 177
19.3%
2 125
13.6%
3 89
 
9.7%
4 66
 
7.2%
5 31
 
3.4%
6 37
 
4.0%
7 25
 
2.7%
8 24
 
2.6%
9 18
 
2.0%
ValueCountFrequency (%)
21 1
 
0.1%
19 1
 
0.1%
17 1
 
0.1%
16 2
 
0.2%
15 3
 
0.3%
14 3
 
0.3%
13 1
 
0.1%
12 5
0.5%
11 8
0.9%
10 12
1.3%

6s
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72707424
Minimum0
Maximum11
Zeros616
Zeros (%)67.2%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2024-11-04T16:05:34.346080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4820487
Coefficient of variation (CV)2.0383733
Kurtosis11.394731
Mean0.72707424
Median Absolute Deviation (MAD)0
Skewness3.0639152
Sum666
Variance2.1964684
MonotonicityNot monotonic
2024-11-04T16:05:34.579744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 616
67.2%
1 154
 
16.8%
2 62
 
6.8%
3 31
 
3.4%
4 20
 
2.2%
5 13
 
1.4%
6 7
 
0.8%
8 4
 
0.4%
7 4
 
0.4%
9 3
 
0.3%
Other values (2) 2
 
0.2%
ValueCountFrequency (%)
0 616
67.2%
1 154
 
16.8%
2 62
 
6.8%
3 31
 
3.4%
4 20
 
2.2%
5 13
 
1.4%
6 7
 
0.8%
7 4
 
0.4%
8 4
 
0.4%
9 3
 
0.3%
ValueCountFrequency (%)
11 1
 
0.1%
10 1
 
0.1%
9 3
 
0.3%
8 4
 
0.4%
7 4
 
0.4%
6 7
 
0.8%
5 13
 
1.4%
4 20
 
2.2%
3 31
3.4%
2 62
6.8%
Distinct494
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Memory size56.7 KiB
2024-11-04T16:05:35.354470image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.2150655
Min length1

Characters and Unicode

Total characters5693
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique365 ?
Unique (%)39.8%

Sample

1st row94.300
2nd row58.300
3rd row89.500
4th row156.300
5th row64.700
ValueCountFrequency (%)
0.000 77
 
8.4%
100.000 43
 
4.7%
50.000 27
 
2.9%
80.000 14
 
1.5%
66.667 10
 
1.1%
133.333 10
 
1.1%
60.000 10
 
1.1%
200.000 10
 
1.1%
25.000 9
 
1.0%
20.000 9
 
1.0%
Other values (484) 697
76.1%
2024-11-04T16:05:36.511249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1720
30.2%
. 910
16.0%
1 542
 
9.5%
3 406
 
7.1%
6 349
 
6.1%
5 344
 
6.0%
7 342
 
6.0%
8 321
 
5.6%
2 287
 
5.0%
4 257
 
4.5%
Other values (2) 215
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1720
30.2%
. 910
16.0%
1 542
 
9.5%
3 406
 
7.1%
6 349
 
6.1%
5 344
 
6.0%
7 342
 
6.0%
8 321
 
5.6%
2 287
 
5.0%
4 257
 
4.5%
Other values (2) 215
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1720
30.2%
. 910
16.0%
1 542
 
9.5%
3 406
 
7.1%
6 349
 
6.1%
5 344
 
6.0%
7 342
 
6.0%
8 321
 
5.6%
2 287
 
5.0%
4 257
 
4.5%
Other values (2) 215
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1720
30.2%
. 910
16.0%
1 542
 
9.5%
3 406
 
7.1%
6 349
 
6.1%
5 344
 
6.0%
7 342
 
6.0%
8 321
 
5.6%
2 287
 
5.0%
4 257
 
4.5%
Other values (2) 215
 
3.8%

Interactions

2024-11-04T16:05:25.233335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:17.951532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:19.586108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:21.027458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:22.509938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:23.830905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:25.436661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:18.353933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:19.834478image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:21.260682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:22.731189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:24.063173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:25.635477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:18.628758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:20.051331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:21.474711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:22.940264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:24.281218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:25.855838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:18.896933image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:20.291943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:21.821435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:23.187363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:24.500257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:26.077771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:19.179338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:20.518286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:22.060300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:23.410019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:24.723483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:26.277160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:19.378052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:20.715337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:22.283397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:23.615133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-04T16:05:24.913100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-04T16:05:36.765723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
4s6sBallsBatting_PositionMatch_BetweenMatch_noRunsTeam_Innings
4s1.0000.4540.809-0.4390.095-0.0060.8910.049
6s0.4541.0000.491-0.1610.0920.0020.6360.064
Balls0.8090.4911.000-0.4080.064-0.0130.9240.027
Batting_Position-0.439-0.161-0.4081.0000.000-0.005-0.3780.000
Match_Between0.0950.0920.0640.0001.0000.9590.0850.654
Match_no-0.0060.002-0.013-0.0050.9591.000-0.0060.150
Runs0.8910.6360.924-0.3780.085-0.0061.0000.063
Team_Innings0.0490.0640.0270.0000.6540.1500.0631.000

Missing values

2024-11-04T16:05:26.564635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-04T16:05:26.945583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Match_noMatch_BetweenTeam_InningsBatsman_NameBatting_PositionDismissalRunsBalls4s6sStrike_Rate
01England vs New ZealandEnglandJonny Bairstow1c Daryl Mitchell b Mitchell Santner33354194.300
11England vs New ZealandEnglandDawid Malan2c Tom Latham b Matt Henry14242058.300
21England vs New ZealandEnglandJoe Root3b Glenn Phillips77864189.500
31England vs New ZealandEnglandHarry Brook4c Devon Conway b Rachin Ravindra251641156.300
41England vs New ZealandEnglandMoeen Ali5b Glenn Phillips11171064.700
51England vs New ZealandEnglandJos Buttler6c Tom Latham b Matt Henry434222102.400
61England vs New ZealandEnglandLiam Livingstone7c Matt Henry b Trent Boult20223090.900
71England vs New ZealandEnglandSam Curran8c Tom Latham b Matt Henry14190073.700
81England vs New ZealandEnglandChris Woakes9c Will Young b Mitchell Santner11121091.700
91England vs New ZealandEnglandAdil Rashid10not out151301115.400
Match_noMatch_BetweenTeam_InningsBatsman_NameBatting_PositionDismissalRunsBalls4s6sStrike_Rate
90648India vs AustraliaIndiaMohammed Shami8c Josh Inglis b Mitchell Starc6101060.000
90748India vs AustraliaIndiaJasprit Bumrah9lbw b Adam Zampa130033.333
90848India vs AustraliaIndiaKuldeep Yadav10run out (Marnus Labuschagne/Pat Cummins)10180055.556
90948India vs AustraliaIndiaMohammed Siraj11not out9810112.500
91048India vs AustraliaAustraliaDavid Warner1c Virat Kohli b Mohammed Shami7310233.333
91148India vs AustraliaAustraliaTravis Head2c Shubman Gill b Mohammed Siraj137120154114.167
91248India vs AustraliaAustraliaMitchell Marsh3c KL Rahul b Jasprit Bumrah151511100.000
91348India vs AustraliaAustraliaSteve Smith4lbw b Jasprit Bumrah491044.444
91448India vs AustraliaAustraliaMarnus Labuschagne5not out581104052.727
91548India vs AustraliaAustraliaGlenn Maxwell6not out2100200.000